{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ek7OfpzzequS"
   },
   "source": [
    "# **载入google drive**\n",
    "这一步可以不执行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 31198,
     "status": "ok",
     "timestamp": 1720950990100,
     "user": {
      "displayName": "Yuanwei Bin",
      "userId": "04820485600131748919"
     },
     "user_tz": -480
    },
    "id": "AuQuqGP4tINW",
    "outputId": "a8adf965-b09d-453d-dcee-07646657a3dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SqRGNIxYlTr7"
   },
   "source": [
    "# **官方版本数据导入**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yHcQ9wurwwFX"
   },
   "source": [
    "赛道一"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "executionInfo": {
     "elapsed": 82228,
     "status": "ok",
     "timestamp": 1720765083865,
     "user": {
      "displayName": "Yuanwei Bin",
      "userId": "04820485600131748919"
     },
     "user_tz": -480
    },
    "id": "oL_v8aw8lZ72",
    "outputId": "2fd464ba-e35f-423a-f157-5b20a5f71e3f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-07-12 06:16:43--  https://drive.usercontent.google.com/download?id=1JwR0Q1ArTg6c47EF2ZuIBpQwCPgXKrO2&export=download&authuser=0&confirm=t&uuid=dc3aa13c-c3a9-458f-983a-8586798cb635&at=APZUnTX25XMxi-z-3wBcgR93IGsL%3A1719235792953\n",
      "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 142.251.12.132, 2404:6800:4003:c11::84\n",
      "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|142.251.12.132|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1084182095 (1.0G) [application/octet-stream]\n",
      "Saving to: ‘Dataset.zip’\n",
      "\n",
      "Dataset.zip         100%[===================>]   1.01G  27.5MB/s    in 45s     \n",
      "\n",
      "2024-07-12 06:17:30 (23.0 MB/s) - ‘Dataset.zip’ saved [1084182095/1084182095]\n",
      "\n",
      "Archive:  Dataset.zip\n",
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     ]
    }
   ],
   "source": [
    "####下载Dataset.zip\n",
    "!wget --header=\"Host: drive.usercontent.google.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Cookie: __Secure-ENID=12.SE=Yd0Bj-CLJ14fnd4qzdJHmwUs4B5zz46UaPC1cPJigNqqFV9PtM2CYyBpSbCkOyzUwzlEdZ1nZFf-igtGi7wSdJ_gqQSfQfh84r9egqFQAy9-GKayCRbdQKdera-2mkpuIT-c64CyR9vfNojM3hxZ9Dej-dGvtxlGjal9ttEHybw; __gsas=ID=ae0421b9a34b478c:T=1710758437:RT=1710758437:S=ALNI_MZP13R9ZOHbCzC0rgHSMrGXj6GCsg; HSID=A-4I-ZudDNUIB6EKH; SSID=A7v_1v9un6xAwVNku; APISID=ctK8IbLjeuDUmgys/AFnMSLWt9KddceDI6; SAPISID=J7GhTwED67EBqJJT/A9nwK7mr0ijGPw08r; __Secure-1PAPISID=J7GhTwED67EBqJJT/A9nwK7mr0ijGPw08r; __Secure-3PAPISID=J7GhTwED67EBqJJT/A9nwK7mr0ijGPw08r; SID=g.a000kgiBabgKCiCYKve9zfoWVgz9eu8sBA6N4XDPPpP5pcW16_C_kzuBV1TvOhAIC8VF1e9fpgACgYKATQSARQSFQHGX2Mi8LXUwWoIwNCEPU8Sy3mXUxoVAUF8yKqGXVfjTGz9gQal7nwGr4Pl0076; __Secure-1PSID=g.a000kgiBabgKCiCYKve9zfoWVgz9eu8sBA6N4XDPPpP5pcW16_C_PDa-DzVmbdGFPyxMQpk9_QACgYKAewSARQSFQHGX2MiAeee4fn0OWglWZfAygqkyBoVAUF8yKp-Sfmtnueimxc-0QbJRF9I0076; __Secure-3PSID=g.a000kgiBabgKCiCYKve9zfoWVgz9eu8sBA6N4XDPPpP5pcW16_C_g9IrMeU98APBo9Stp6wEnAACgYKAQASARQSFQHGX2MiFWtc9ucONXnpxBzlRdudEhoVAUF8yKoeZwCpJDnjfAFjGssHSUGm0076; NID=515=GQhY9nKKFCx3qFDjE0MA4ubjWNdef6xCIY_RfWOPWKEtyfBN3nAUl8WHI2VczjNQ4rVkj1XBAY8WNWHXyqSK10CfT4FxsFlPzrHIJpeTtm1nWRNBd9AAfBKJHz4XpESszntVUTE_59RklZuKo0vk1poReVi2da1PZKC3CTKH2Ll3gB5xuB9wf4bmq8ylVUuIROPJczr0XnCuUHV3qLdBvgy9_870b6UwOq1iOlIxFQFm01EZ4pqF4q1Ub3QRSWpEMLh4LSZFpJ5O255R5OV7krmEdDvH_sHoTEPZAg2PoEpwAyGK6Xp9qcLIlldgx5-5V86N8Wtb93uTlQuA_CFXb5_2eP3bgeX8txwlJ5SrldVjg9ctzYtBU2RwJKTSvdHfIG7lpOkg6XlkvDOcJpR3DihT_OlqnPn7drCAJpvVDv29hZn5XPMXaSrNdbG64OJ9urJEw5odEwsLYkkpC1vmlUcuoo52S5f6RQu0Z8kZiV8iRW6XIqHsSmQHunVaxk6xWCStUg; __Secure-1PSIDTS=sidts-CjEB3EgAEtTS0OazynCofIH4RCBstiRP5flEcvYW3z4Fg9oGd5QOESDOZt1wO2iqUYHjEAA; __Secure-3PSIDTS=sidts-CjEB3EgAEtTS0OazynCofIH4RCBstiRP5flEcvYW3z4Fg9oGd5QOESDOZt1wO2iqUYHjEAA; SIDCC=AKEyXzVI6aMX8lSDja86Yts3FBAtBzPCzVNgaX5BCz78NWsWzlT3yFWKUV7ZE46SFzE1GiBI-cHdTw; __Secure-1PSIDCC=AKEyXzUo4NQAwqqPMxP2eye-MFEbZmBIm_sZqRU1amttg0YoQkc8ZKSNXdHl5jNCMEbhrUHhS9-K; __Secure-3PSIDCC=AKEyXzWf2lIdmDLeZKpXSi9GytVQb6XudrYiNUBA5gW952YuLh8kL6T3IbBlu8zOTfGEcdUp5O1R\" --header=\"Connection: keep-alive\" \"https://drive.usercontent.google.com/download?id=1JwR0Q1ArTg6c47EF2ZuIBpQwCPgXKrO2&export=download&authuser=0&confirm=t&uuid=dc3aa13c-c3a9-458f-983a-8586798cb635&at=APZUnTX25XMxi-z-3wBcgR93IGsL%3A1719235792953\" -c -O 'Dataset.zip'\n",
    "####解压Dataset.zip\n",
    "!unzip Dataset.zip\n",
    "####删除Dataset.zip\n",
    "!rm Dataset.zip\n",
    "####重命名Training_data文件名\n",
    "!mv Dataset/Training_data Dataset/Trainset_track_A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hjHXYCzgw2v5"
   },
   "source": [
    "赛道二"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "executionInfo": {
     "elapsed": 231508,
     "status": "ok",
     "timestamp": 1720765946210,
     "user": {
      "displayName": "Yuanwei Bin",
      "userId": "04820485600131748919"
     },
     "user_tz": -480
    },
    "id": "B88H3zRrnfil",
    "outputId": "b9243c63-5134-40a3-846b-910ab4a657b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-07-12 06:28:36--  https://ai-studio-online.bj.bcebos.com/v1/38e9adf0fce84527aad3558cc3e82d0e9a251aac4c934297afae9b74d9b3d1e9?responseContentDisposition=attachment%3B%20filename%3Dtrain_track_B.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-06-04T03%3A21%3A02Z%2F-1%2F%2Facd359add161bace603a52c7a268467406cb3c1889a7114bbb687de8002b55f6\n",
      "Resolving ai-studio-online.bj.bcebos.com (ai-studio-online.bj.bcebos.com)... 103.235.47.176, 2409:8c04:1001:1203:0:ff:b0bb:4f27\n",
      "Connecting to ai-studio-online.bj.bcebos.com (ai-studio-online.bj.bcebos.com)|103.235.47.176|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 4740031429 (4.4G) [application/octet-stream]\n",
      "Saving to: ‘train_track_B.zip’\n",
      "\n",
      "train_track_B.zip   100%[===================>]   4.41G  27.9MB/s    in 2m 48s  \n",
      "\n",
      "2024-07-12 06:31:25 (26.9 MB/s) - ‘train_track_B.zip’ saved [4740031429/4740031429]\n",
      "\n",
      "Archive:  train_track_B.zip\n",
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     ]
    }
   ],
   "source": [
    "####下载train_track_B.zip\n",
    "!wget --header=\"Host: ai-studio-online.bj.bcebos.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Referer: https://aistudio.baidu.com/\" \"https://ai-studio-online.bj.bcebos.com/v1/38e9adf0fce84527aad3558cc3e82d0e9a251aac4c934297afae9b74d9b3d1e9?responseContentDisposition=attachment%3B%20filename%3Dtrain_track_B.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-06-04T03%3A21%3A02Z%2F-1%2F%2Facd359add161bace603a52c7a268467406cb3c1889a7114bbb687de8002b55f6\" -c -O 'train_track_B.zip'\n",
    "####解压到train_track_B\n",
    "!mkdir -p train_track_B && unzip -o train_track_B.zip -d train_track_B/\n",
    "####将train_track_B移到Dataset下\n",
    "!mv train_track_B Dataset/Trainset_track_B\n",
    "####删除train_track_B.zip\n",
    "!rm train_track_B.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J6DqAplSlOd1"
   },
   "source": [
    "# **额外数据导入**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2RNGoSyovQpc"
   },
   "source": [
    "导入赛道二额外数据\n",
    "\n",
    "数据来源：https://github.com/Mohamedelrefaie/DrivAerNet\n",
    "\n",
    "论文：Elrefaie, Mohamed, Angela Dai, and Faez Ahmed. \"Drivaernet: A parametric car dataset for data-driven aerodynamic design and graph-based drag prediction.\" arXiv preprint arXiv:2403.08055 (2024).\n",
    "\n",
    "特别说明：额外数据中仅使用了id>745的数据，未踩到比赛测试数据，数据预处理见“centroidPressureFromDrivAerNet.py”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "De2aJjkzlM2k"
   },
   "outputs": [],
   "source": [
    "# import gdown\n",
    "# import os\n",
    "# import tarfile\n",
    "\n",
    "# ###################### npy_dataset_id_to2090 ######################\n",
    "# file_id = '1HrlAFfxmvidh4nmo5OA97hn7T1w2D3W2' # 文件ID\n",
    "# destination = '../npy_dataset_id_to2090.tar.gz' # 文件下载路径\n",
    "# download_url = f'https://drive.google.com/uc?id={file_id}' # 构建下载URL\n",
    "# if not os.path.exists(destination):\n",
    "#   gdown.download(download_url, destination, quiet=False) # 下载文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###################### 第一部分：npy_dataset_id_to2090 ######################\n",
    "!curl 'https://drive.usercontent.google.com/download?id=1HrlAFfxmvidh4nmo5OA97hn7T1w2D3W2&export=download&authuser=0&confirm=t&uuid=fa8ce3e5-8e5e-47f0-bc88-a5255bb1d205&at=APZUnTX5vfaU7XGNnWVJO-3d3NnS%3A1721008623935' \\\n",
    "  -H 'accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7' \\\n",
    "  -H 'accept-language: zh-CN,zh;q=0.9,en;q=0.8,de;q=0.7' \\\n",
    "  -H 'cookie: SID=g.a000lgi8bRQnt3R6LXRzzKoIKj5y34h1bzpwUabi2WXA1TVd6wJ_P1oOKYaH1BLrHCo3iS0ORAACgYKARkSARESFQHGX2MiWPVwYo8_viuWERHrHR1NdhoVAUF8yKruIZ_4e1ei2Ekggpa8y8tn0076; __Secure-1PSID=g.a000lgi8bRQnt3R6LXRzzKoIKj5y34h1bzpwUabi2WXA1TVd6wJ_N4Ewz7Dr3VAo_wx2LK2r8gACgYKATwSARESFQHGX2MiQ8tfWf2JaqKvrnUyQ45v3BoVAUF8yKq8rQ4q5pVZQVPFSWPsFpAL0076; __Secure-3PSID=g.a000lgi8bRQnt3R6LXRzzKoIKj5y34h1bzpwUabi2WXA1TVd6wJ_2Uk70D_VaOy1CAOn_-q02wACgYKAdMSARESFQHGX2Milt8gLaNlTE2LPd0GCHBY8RoVAUF8yKpwkzBbaFeOpamEruSyO4KB0076; HSID=Aze2C-18CaajQB5ZY; SSID=AcSGa_zv1UHXYWXjD; APISID=63spz5_adFMvKs3X/AnRG94TS2UMLY3-Jn; SAPISID=lv3zXMc-MpsJeRZr/ARn4Lxk4FrjjMbdSd; __Secure-1PAPISID=lv3zXMc-MpsJeRZr/ARn4Lxk4FrjjMbdSd; __Secure-3PAPISID=lv3zXMc-MpsJeRZr/ARn4Lxk4FrjjMbdSd; AEC=AVYB7cqCjRuyMKwjdDYgIZrykK3GrPvfMCHPw6aftmy-i3jUaK-D4PahKUY; NID=515=qmBvly9f2yPayL5i0BHfVfz668yIlBHAnFQ5N3qoP2gVxqUWPJSsLoUDxXX25G2CDW_FvJW3NOTZDjiCnxDnUQhYDsqIfwa82Zh6xWwzC43u0L25cZKfNGerS4-eyAuiQbBgUl8Rf3PYriRkKJoCMs25jhZ_9z0Wuvch5zkc5zJoL1w9NBRjbk-F6HB9GTuSTBEX-uBWQFQDH5sWuQxwsXtSGRZPQOkKPUNF-LWGa26P1eP2syJlBSOLXIEj2J30p-ahuTAwSgkisPo5YTPFh6gX9iBztpmEyyx9CF4OESec830Sxcu-DgkeNBIwGeKapNLRdg-hyUA6HE2T-tVWQEyhv0k-B5aq95ig81bpWRwDsfUCW8f22q1eZM4Js5nw6nND2hQwwfJ9RlA6orbvTm8DIZZf1ZvwDf1ooTLMA3TZeOv2PEvtlYIhBLX_JxcY_hIc_qvgYrRsJkwOKkykafiRw6F58W9lIQ; __Secure-1PSIDTS=sidts-CjEB4E2dkSE6NJDghNHP1m15TJhjhK_3gk6CbiZHkVbttFbJCYIB2BwTObN5Q77lMAC4EAA; __Secure-3PSIDTS=sidts-CjEB4E2dkSE6NJDghNHP1m15TJhjhK_3gk6CbiZHkVbttFbJCYIB2BwTObN5Q77lMAC4EAA; SIDCC=AKEyXzXx3xq69yhLhL6faYN-CslMkFWFN9k2VGCpxUBix4n1USKcT0K9k1kr9z4WWKxOSWsLX9pf; __Secure-1PSIDCC=AKEyXzVJ1otwaPYrh7P23QePj9Uci7oNGp8LoptrV4SuIpcFNgvHA4BhonL7WRlE7TTsE-FeNO0; __Secure-3PSIDCC=AKEyXzV2hkupH10qZ9QKzyDBrGprWcq6ovH37ctI_OU7s25hBteNzXDK3IHj0Im1nsm4DhqdYKY' \\\n",
    "  -H 'priority: u=0, i' \\\n",
    "  -H 'sec-ch-ua: \"Not/A)Brand\";v=\"8\", \"Chromium\";v=\"126\", \"Google Chrome\";v=\"126\"' \\\n",
    "  -H 'sec-ch-ua-arch: \"x86\"' \\\n",
    "  -H 'sec-ch-ua-bitness: \"64\"' \\\n",
    "  -H 'sec-ch-ua-form-factors: \"Desktop\"' \\\n",
    "  -H 'sec-ch-ua-full-version: \"126.0.6478.127\"' \\\n",
    "  -H 'sec-ch-ua-full-version-list: \"Not/A)Brand\";v=\"8.0.0.0\", \"Chromium\";v=\"126.0.6478.127\", \"Google Chrome\";v=\"126.0.6478.127\"' \\\n",
    "  -H 'sec-ch-ua-mobile: ?0' \\\n",
    "  -H 'sec-ch-ua-model: \"\"' \\\n",
    "  -H 'sec-ch-ua-platform: \"Windows\"' \\\n",
    "  -H 'sec-ch-ua-platform-version: \"15.0.0\"' \\\n",
    "  -H 'sec-ch-ua-wow64: ?0' \\\n",
    "  -H 'sec-fetch-dest: document' \\\n",
    "  -H 'sec-fetch-mode: navigate' \\\n",
    "  -H 'sec-fetch-site: cross-site' \\\n",
    "  -H 'sec-fetch-user: ?1' \\\n",
    "  -H 'upgrade-insecure-requests: 1' \\\n",
    "  -H 'user-agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36' \\\n",
    "  -H 'x-client-data: CK61yQEIlbbJAQimtskBCKmdygEIsvXKAQiWocsBCJz+zAEI7ZjNAQiFoM0BCKaizgEIg6jOAQ==' -o ../npy_dataset_id_to2090.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tZRuUMtu4Cji"
   },
   "outputs": [],
   "source": [
    "tar_path = '../npy_dataset_id_to2090.tar.gz' # 压缩文件路径\n",
    "extract_dir = '../Dataset/Extra_Trainset_track_B' # 解压目录\n",
    "!tar -xzf tar_path --strip-components=1 -C extract_dir\n",
    "!rm tar_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Tusvl6Ox4Tf7"
   },
   "outputs": [],
   "source": [
    "# ###################### npy_dataset_last_part ######################\n",
    "# file_id = '13LAHqAnjYpqcL33_PdBv71YiVTRi6Bgh' # 文件ID\n",
    "# destination = '../npy_dataset_last_part.tar.gz' # 文件下载路径\n",
    "# download_url = f'https://drive.google.com/uc?id={file_id}' # 构建下载URL\n",
    "# if not os.path.exists(destination):\n",
    "#   gdown.download(download_url, destination, quiet=False) # 下载文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###################### 第二部分：npy_dataset_last_part ######################\n",
    "!curl 'https://drive.usercontent.google.com/download?id=13LAHqAnjYpqcL33_PdBv71YiVTRi6Bgh&export=download&authuser=0&confirm=t&uuid=ecc5c29b-d551-4cae-955b-8bb3f5162f3f&at=APZUnTXV6vdX6nTQxVdPMOk0foy5%3A1721008953098' \\\n",
    "  -H 'accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7' \\\n",
    "  -H 'accept-language: zh-CN,zh;q=0.9,en;q=0.8,de;q=0.7' \\\n",
    "  -H 'cookie: SID=g.a000lgi8bRQnt3R6LXRzzKoIKj5y34h1bzpwUabi2WXA1TVd6wJ_P1oOKYaH1BLrHCo3iS0ORAACgYKARkSARESFQHGX2MiWPVwYo8_viuWERHrHR1NdhoVAUF8yKruIZ_4e1ei2Ekggpa8y8tn0076; __Secure-1PSID=g.a000lgi8bRQnt3R6LXRzzKoIKj5y34h1bzpwUabi2WXA1TVd6wJ_N4Ewz7Dr3VAo_wx2LK2r8gACgYKATwSARESFQHGX2MiQ8tfWf2JaqKvrnUyQ45v3BoVAUF8yKq8rQ4q5pVZQVPFSWPsFpAL0076; __Secure-3PSID=g.a000lgi8bRQnt3R6LXRzzKoIKj5y34h1bzpwUabi2WXA1TVd6wJ_2Uk70D_VaOy1CAOn_-q02wACgYKAdMSARESFQHGX2Milt8gLaNlTE2LPd0GCHBY8RoVAUF8yKpwkzBbaFeOpamEruSyO4KB0076; HSID=Aze2C-18CaajQB5ZY; SSID=AcSGa_zv1UHXYWXjD; APISID=63spz5_adFMvKs3X/AnRG94TS2UMLY3-Jn; SAPISID=lv3zXMc-MpsJeRZr/ARn4Lxk4FrjjMbdSd; __Secure-1PAPISID=lv3zXMc-MpsJeRZr/ARn4Lxk4FrjjMbdSd; __Secure-3PAPISID=lv3zXMc-MpsJeRZr/ARn4Lxk4FrjjMbdSd; AEC=AVYB7cqCjRuyMKwjdDYgIZrykK3GrPvfMCHPw6aftmy-i3jUaK-D4PahKUY; NID=515=qmBvly9f2yPayL5i0BHfVfz668yIlBHAnFQ5N3qoP2gVxqUWPJSsLoUDxXX25G2CDW_FvJW3NOTZDjiCnxDnUQhYDsqIfwa82Zh6xWwzC43u0L25cZKfNGerS4-eyAuiQbBgUl8Rf3PYriRkKJoCMs25jhZ_9z0Wuvch5zkc5zJoL1w9NBRjbk-F6HB9GTuSTBEX-uBWQFQDH5sWuQxwsXtSGRZPQOkKPUNF-LWGa26P1eP2syJlBSOLXIEj2J30p-ahuTAwSgkisPo5YTPFh6gX9iBztpmEyyx9CF4OESec830Sxcu-DgkeNBIwGeKapNLRdg-hyUA6HE2T-tVWQEyhv0k-B5aq95ig81bpWRwDsfUCW8f22q1eZM4Js5nw6nND2hQwwfJ9RlA6orbvTm8DIZZf1ZvwDf1ooTLMA3TZeOv2PEvtlYIhBLX_JxcY_hIc_qvgYrRsJkwOKkykafiRw6F58W9lIQ; __Secure-1PSIDTS=sidts-CjEB4E2dkSE6NJDghNHP1m15TJhjhK_3gk6CbiZHkVbttFbJCYIB2BwTObN5Q77lMAC4EAA; __Secure-3PSIDTS=sidts-CjEB4E2dkSE6NJDghNHP1m15TJhjhK_3gk6CbiZHkVbttFbJCYIB2BwTObN5Q77lMAC4EAA; SIDCC=AKEyXzXxsM9syaIZNUoIGb2zN2lXB7rG8BpEsm9SJz87gq5gsNxwFe-lG63U6c74LYoXvbopHG0Z; __Secure-1PSIDCC=AKEyXzVyhAwFNnt6SkT2HO83gFjVIK8IxNxnondGM0u1COHMa-GBI0MSe0gjD1pG0cphSFwVl9s; __Secure-3PSIDCC=AKEyXzURcwg9KPPeDlIcZSKmqW6O9S42naV7FjBARhx6Zv3vCYTg-EdHEYgQl6tri4LgM_w-DFc' \\\n",
    "  -H 'priority: u=0, i' \\\n",
    "  -H 'sec-ch-ua: \"Not/A)Brand\";v=\"8\", \"Chromium\";v=\"126\", \"Google Chrome\";v=\"126\"' \\\n",
    "  -H 'sec-ch-ua-arch: \"x86\"' \\\n",
    "  -H 'sec-ch-ua-bitness: \"64\"' \\\n",
    "  -H 'sec-ch-ua-form-factors: \"Desktop\"' \\\n",
    "  -H 'sec-ch-ua-full-version: \"126.0.6478.127\"' \\\n",
    "  -H 'sec-ch-ua-full-version-list: \"Not/A)Brand\";v=\"8.0.0.0\", \"Chromium\";v=\"126.0.6478.127\", \"Google Chrome\";v=\"126.0.6478.127\"' \\\n",
    "  -H 'sec-ch-ua-mobile: ?0' \\\n",
    "  -H 'sec-ch-ua-model: \"\"' \\\n",
    "  -H 'sec-ch-ua-platform: \"Windows\"' \\\n",
    "  -H 'sec-ch-ua-platform-version: \"15.0.0\"' \\\n",
    "  -H 'sec-ch-ua-wow64: ?0' \\\n",
    "  -H 'sec-fetch-dest: document' \\\n",
    "  -H 'sec-fetch-mode: navigate' \\\n",
    "  -H 'sec-fetch-site: cross-site' \\\n",
    "  -H 'sec-fetch-user: ?1' \\\n",
    "  -H 'upgrade-insecure-requests: 1' \\\n",
    "  -H 'user-agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36' \\\n",
    "  -H 'x-client-data: CK61yQEIlbbJAQimtskBCKmdygEIsvXKAQiWocsBCJz+zAEI7ZjNAQiFoM0BCKaizgEIg6jOAQ==' -o ../npy_dataset_last_part.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3fu-nayY3YZ-"
   },
   "outputs": [],
   "source": [
    "tar_path = '../npy_dataset_last_part.tar.gz' # 压缩文件路径\n",
    "extract_dir = '../Dataset/Extra_Trainset_track_B' # 解压目录\n",
    "!tar -xzf tar_path --strip-components=1 -C extract_dir\n",
    "!rm tar_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ml0rN3NHvT8t"
   },
   "source": [
    "导入PointBERT预训练模型\n",
    "\n",
    "数据来源：https://github.com/salesforce/ULIP\n",
    "\n",
    "论文：Xue, Le, et al. \"Ulip-2: Towards scalable multimodal pre-training for 3d understanding.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 685
    },
    "collapsed": true,
    "executionInfo": {
     "elapsed": 3040,
     "status": "error",
     "timestamp": 1720840163996,
     "user": {
      "displayName": "Yuanwei Bin",
      "userId": "04820485600131748919"
     },
     "user_tz": -480
    },
    "id": "3dyCaPRjvabD",
    "outputId": "f19a4795-eff5-482e-b8e5-a0d863cb1857"
   },
   "outputs": [],
   "source": [
    "# import gdown\n",
    "# import os\n",
    "# download_url = 'https://drive.google.com/uc?id=1OYwnUE4XUBQD3OOgCk_Ba6YR0SoKOT1L'\n",
    "# destination = 'geom/ckpt/pretrained_models_ckpt_zero-sho_classification_checkpoint_pointbert.pt'\n",
    "# if not os.path.exists(destination):\n",
    "#   gdown.download(download_url, destination, quiet=False) # 下载文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!curl 'https://ffbea29b5d69e57cd3f99698f0d1e6cb46b8edd36b9ed48db5481d7-apidata.googleusercontent.com/download/storage/v1/b/sfr-ulip-code-release-research/o/pretrained_models%2Fckpt_zero-sho_classification%2Fcheckpoint_pointbert.pt?jk=AYBlUPBLCXeVNJNMXlF3AOe1Gbqeigd6KJL02kS7xCOQTiVb8zCXAjbO4aBIDQcnxFEOCt2LTE8jHVZL3JGyQzFnXbNFB588nktCcbwjdoihbVEyk1GQf38cXrQKf2kKuR6fgUkRaXBsY5GlRV2rXH9HobVjGdFHqH6L2K60s5ZwduEwpoGPfAHMY5YiNhYkd07dOzQnkwLPwppfipVkvGTqv15IWJd0RzXajYyZxYDAg98rrm7Y6TxGWRQ-xF4PXKVym_WUoUTRK8sb-TjIk2ehvh0MMh4vcCr7x3CuAtd-dR_tPpG08aGnG-fwzmRO-hxRrP967oMqqNTL-CQYi-0cVB5DR7DCQfqe9CxZEp41bcH8KbruzD223Aal2o2Fwygy8Vo1Zly60i6H53wO8Nu3gq-yToihPEvnoc468YLllvMtgLNdFyp7v6U-aWAJ_iwLMeLROhDdSwysUSdbsX14DLUSGnsU6sPxEONVcO7wkB-iYrE-vlqlV9IE6Prwy86U8kspsxam46eLgz5PsyYFzugBOQ9_-Nj5_f3a6Ni_JnCzPGrEe7nCEJLGebDRxWxGXPXsmICFzLnohe_7jWTa4vr43PfEbb5UFaEzyA2Sgtlw1w85ek-yl9jymF1SlbWdlxizq_zolZ5g0dqo3UAhoICBuVfJAbHKQW2Go6i1rV0KMbAK3eh17PkhWE5RvQ1xTnf4GJtJvK2QSoJVh1-gWXlY2RjxcDju3XFTBxIJQSCw9Qb4AEP-TvlQEqNiMOrjeiy77pb8JIoWSU4YwsedOgOca71LtQIVCT8wcc19HKKI2-PnjujbmiGDY2oO1_yoqfNYShYjUdBBD6L-u2H3AqJm0rywVfMDnYULc1yxMfwzoo5Fd7gJC_wnaRm6w1fWl3arwriuUWsL3MS-in8ZTO7FmXk9eP1LnF6MCXHhd06y-67MHisZpjHmk9TBVNs1ZmgLXrwABsNLHVIJ3xfDxHuOjZ074iDy8KqBnzfwG8c4I2HAnePK76z6b4gVDEk7v8iAo4JDQ5PQI9Bi1PP3CKPMtTf7fKzmopoJ96VNBwUXI6feYtFZaLYo_vbeFh7nSsfRCRXlKL0d8zcb5un_4Dtd_J0IMoYEJ58ppxZi9TVBA1tlQwCHmKs1w4XWBD6xGiuj_tUizrtYnIQDVY-ZthPK9rXWl8J4g5R6EKZkvb7AQtltPXaFhZYHt4gkt016IybxmplUMpUbCwI5JsPVcdYFwshW_LawNmQGnBON_i0nQNlmQ481DRtPkQK6dhw&isca=1' \\\n",
    "  -H 'accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7' \\\n",
    "  -H 'accept-language: zh-CN,zh;q=0.9,en;q=0.8,de;q=0.7' \\\n",
    "  -H 'priority: u=0, i' \\\n",
    "  -H 'referer: https://github.com/salesforce/ULIP?tab=readme-ov-file' \\\n",
    "  -H 'sec-ch-ua: \"Not/A)Brand\";v=\"8\", \"Chromium\";v=\"126\", \"Google Chrome\";v=\"126\"' \\\n",
    "  -H 'sec-ch-ua-arch: \"x86\"' \\\n",
    "  -H 'sec-ch-ua-bitness: \"64\"' \\\n",
    "  -H 'sec-ch-ua-full-version-list: \"Not/A)Brand\";v=\"8.0.0.0\", \"Chromium\";v=\"126.0.6478.127\", \"Google Chrome\";v=\"126.0.6478.127\"' \\\n",
    "  -H 'sec-ch-ua-mobile: ?0' \\\n",
    "  -H 'sec-ch-ua-model: \"\"' \\\n",
    "  -H 'sec-ch-ua-platform: \"Windows\"' \\\n",
    "  -H 'sec-ch-ua-platform-version: \"15.0.0\"' \\\n",
    "  -H 'sec-ch-ua-wow64: ?0' \\\n",
    "  -H 'sec-fetch-dest: document' \\\n",
    "  -H 'sec-fetch-mode: navigate' \\\n",
    "  -H 'sec-fetch-site: cross-site' \\\n",
    "  -H 'sec-fetch-user: ?1' \\\n",
    "  -H 'upgrade-insecure-requests: 1' \\\n",
    "  -H 'user-agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36' \\\n",
    "  -H 'x-client-data: CK61yQEIlbbJAQimtskBCKmdygEIsvXKAQiWocsBCJz+zAEI7ZjNAQiFoM0BCKaizgEIg6jOAQ==' -o geom/ckpt/pretrained_models_ckpt_zero-sho_classification_checkpoint_pointbert.pt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_a6z3N4he7zl"
   },
   "source": [
    "# **安装包**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "executionInfo": {
     "elapsed": 51585,
     "status": "ok",
     "timestamp": 1720768053599,
     "user": {
      "displayName": "Yuanwei Bin",
      "userId": "04820485600131748919"
     },
     "user_tz": -480
    },
    "id": "qSqqyPps0Jgx",
    "outputId": "779ef683-85ac-409f-ce3b-e3cce481345e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fatal: destination path 'KNN_CUDA' already exists and is not an empty directory.\n",
      "pip3 install -r requirements.txt\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 1)) (1.25.2)\n",
      "Requirement already satisfied: scikit_learn in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 2)) (1.2.2)\n",
      "Requirement already satisfied: torch>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 3)) (2.3.0+cu121)\n",
      "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit_learn->-r requirements.txt (line 2)) (1.11.4)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit_learn->-r requirements.txt (line 2)) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit_learn->-r requirements.txt (line 2)) (3.5.0)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->-r requirements.txt (line 3)) (3.15.4)\n",
      "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->-r requirements.txt (line 3)) (4.12.2)\n",
      "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->-r requirements.txt (line 3)) (1.12.1)\n",
      "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->-r requirements.txt (line 3)) (3.3)\n",
      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->-r requirements.txt (line 3)) (3.1.4)\n",
      "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->-r requirements.txt (line 3)) (2023.6.0)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->-r requirements.txt (line 3)) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->-r requirements.txt (line 3)) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->-r requirements.txt (line 3)) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->-r requirements.txt (line 3)) (8.9.2.26)\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->-r requirements.txt (line 3)) (12.1.3.1)\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->-r requirements.txt (line 3)) (11.0.2.54)\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->-r requirements.txt (line 3)) (10.3.2.106)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->-r requirements.txt (line 3)) (11.4.5.107)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->-r requirements.txt (line 3)) (12.1.0.106)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->-r requirements.txt (line 3)) (2.20.5)\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->-r requirements.txt (line 3)) (12.1.105)\n",
      "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->-r requirements.txt (line 3)) (2.3.0)\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.1.0->-r requirements.txt (line 3)) (12.5.82)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.1.0->-r requirements.txt (line 3)) (2.1.5)\n",
      "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.1.0->-r requirements.txt (line 3)) (1.3.0)\n",
      "python3 setup.py bdist_wheel\n",
      "/usr/local/lib/python3.10/dist-packages/torch/utils/cpp_extension.py:1967: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. \n",
      "If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].\n",
      "  warnings.warn(\n",
      "running bdist_wheel\n",
      "running build\n",
      "running build_py\n",
      "creating build\n",
      "creating build/lib\n",
      "creating build/lib/knn_cuda\n",
      "copying knn_cuda/__init__.py -> build/lib/knn_cuda\n",
      "creating build/lib/knn_cuda/csrc\n",
      "creating build/lib/knn_cuda/csrc/cuda\n",
      "copying knn_cuda/csrc/cuda/knn.cu -> build/lib/knn_cuda/csrc/cuda\n",
      "copying knn_cuda/csrc/cuda/knn.cpp -> build/lib/knn_cuda/csrc/cuda\n",
      "/usr/local/lib/python3.10/dist-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n",
      "!!\n",
      "\n",
      "        ********************************************************************************\n",
      "        Please avoid running ``setup.py`` directly.\n",
      "        Instead, use pypa/build, pypa/installer, pypa/build or\n",
      "        other standards-based tools.\n",
      "\n",
      "        See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n",
      "        ********************************************************************************\n",
      "\n",
      "!!\n",
      "  self.initialize_options()\n",
      "installing to build/bdist.linux-x86_64/wheel\n",
      "running install\n",
      "running install_lib\n",
      "creating build/bdist.linux-x86_64\n",
      "creating build/bdist.linux-x86_64/wheel\n",
      "creating build/bdist.linux-x86_64/wheel/knn_cuda\n",
      "copying build/lib/knn_cuda/__init__.py -> build/bdist.linux-x86_64/wheel/knn_cuda\n",
      "creating build/bdist.linux-x86_64/wheel/knn_cuda/csrc\n",
      "creating build/bdist.linux-x86_64/wheel/knn_cuda/csrc/cuda\n",
      "copying build/lib/knn_cuda/csrc/cuda/knn.cu -> build/bdist.linux-x86_64/wheel/knn_cuda/csrc/cuda\n",
      "copying build/lib/knn_cuda/csrc/cuda/knn.cpp -> build/bdist.linux-x86_64/wheel/knn_cuda/csrc/cuda\n",
      "running install_egg_info\n",
      "running egg_info\n",
      "creating KNN_CUDA.egg-info\n",
      "writing KNN_CUDA.egg-info/PKG-INFO\n",
      "writing dependency_links to KNN_CUDA.egg-info/dependency_links.txt\n",
      "writing requirements to KNN_CUDA.egg-info/requires.txt\n",
      "writing top-level names to KNN_CUDA.egg-info/top_level.txt\n",
      "writing manifest file 'KNN_CUDA.egg-info/SOURCES.txt'\n",
      "reading manifest file 'KNN_CUDA.egg-info/SOURCES.txt'\n",
      "writing manifest file 'KNN_CUDA.egg-info/SOURCES.txt'\n",
      "Copying KNN_CUDA.egg-info to build/bdist.linux-x86_64/wheel/KNN_CUDA-0.2-py3.10.egg-info\n",
      "running install_scripts\n",
      "creating build/bdist.linux-x86_64/wheel/KNN_CUDA-0.2.dist-info/WHEEL\n",
      "creating 'dist/KNN_CUDA-0.2-py3-none-any.whl' and adding 'build/bdist.linux-x86_64/wheel' to it\n",
      "adding 'knn_cuda/__init__.py'\n",
      "adding 'knn_cuda/csrc/cuda/knn.cpp'\n",
      "adding 'knn_cuda/csrc/cuda/knn.cu'\n",
      "adding 'KNN_CUDA-0.2.dist-info/METADATA'\n",
      "adding 'KNN_CUDA-0.2.dist-info/WHEEL'\n",
      "adding 'KNN_CUDA-0.2.dist-info/top_level.txt'\n",
      "adding 'KNN_CUDA-0.2.dist-info/RECORD'\n",
      "removing build/bdist.linux-x86_64/wheel\n",
      "pip3 install --upgrade dist/*.whl\n",
      "Processing ./dist/KNN_CUDA-0.2-py3-none-any.whl\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from KNN-CUDA==0.2) (1.25.2)\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from KNN-CUDA==0.2) (1.2.2)\n",
      "Requirement already satisfied: torch>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from KNN-CUDA==0.2) (2.3.0+cu121)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->KNN-CUDA==0.2) (3.15.4)\n",
      "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->KNN-CUDA==0.2) (4.12.2)\n",
      "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->KNN-CUDA==0.2) (1.12.1)\n",
      "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->KNN-CUDA==0.2) (3.3)\n",
      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->KNN-CUDA==0.2) (3.1.4)\n",
      "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->KNN-CUDA==0.2) (2023.6.0)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->KNN-CUDA==0.2) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->KNN-CUDA==0.2) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->KNN-CUDA==0.2) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->KNN-CUDA==0.2) (8.9.2.26)\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->KNN-CUDA==0.2) (12.1.3.1)\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->KNN-CUDA==0.2) (11.0.2.54)\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->KNN-CUDA==0.2) (10.3.2.106)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->KNN-CUDA==0.2) (11.4.5.107)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->KNN-CUDA==0.2) (12.1.0.106)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->KNN-CUDA==0.2) (2.20.5)\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->KNN-CUDA==0.2) (12.1.105)\n",
      "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.1.0->KNN-CUDA==0.2) (2.3.0)\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.1.0->KNN-CUDA==0.2) (12.5.82)\n",
      "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->KNN-CUDA==0.2) (1.11.4)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->KNN-CUDA==0.2) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->KNN-CUDA==0.2) (3.5.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.1.0->KNN-CUDA==0.2) (2.1.5)\n",
      "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.1.0->KNN-CUDA==0.2) (1.3.0)\n",
      "KNN-CUDA is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n"
     ]
    }
   ],
   "source": [
    "# 克隆仓库\n",
    "!git clone https://github.com/unlimblue/KNN_CUDA.git\n",
    "\n",
    "# 进入目录\n",
    "import os\n",
    "os.chdir('KNN_CUDA')\n",
    "\n",
    "# 运行 make 和 make install\n",
    "!make && make install"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 371,
     "status": "ok",
     "timestamp": 1720951015669,
     "user": {
      "displayName": "Yuanwei Bin",
      "userId": "04820485600131748919"
     },
     "user_tz": -480
    },
    "id": "VbXn9Hct43Fm",
    "outputId": "66e4476c-4dd7-4627-cd95-23445ed7ffb9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1720951018268,
     "user": {
      "displayName": "Yuanwei Bin",
      "userId": "04820485600131748919"
     },
     "user_tz": -480
    },
    "id": "JTNDQI1pu5Do",
    "outputId": "9129ccf4-33ea-4604-e632-2473d9acbe89"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当前工作目录是: /content/drive/MyDrive/Colab Notebooks/GeoCAD_V2\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "target_directory = 'drive/MyDrive/Colab Notebooks/GeoCAD_V2'\n",
    "os.chdir(target_directory)\n",
    "####这里要确保工作目录在GeoCAD_V2下\n",
    "print(\"当前工作目录是:\", os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "executionInfo": {
     "elapsed": 102738,
     "status": "ok",
     "timestamp": 1720951124764,
     "user": {
      "displayName": "Yuanwei Bin",
      "userId": "04820485600131748919"
     },
     "user_tz": -480
    },
    "id": "nFuaNIEpudK-",
    "outputId": "d3039b16-03fd-4f1b-f73d-2cfad24bafd2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting timm\n",
      "  Downloading timm-1.0.7-py3-none-any.whl (2.3 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from timm) (2.3.0+cu121)\n",
      "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from timm) (0.18.0+cu121)\n",
      "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from timm) (6.0.1)\n",
      "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (from timm) (0.23.4)\n",
      "Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from timm) (0.4.3)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->timm) (3.15.4)\n",
      "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->timm) (2023.6.0)\n",
      "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->timm) (24.1)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->timm) (2.31.0)\n",
      "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->timm) (4.66.4)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->timm) (4.12.2)\n",
      "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->timm) (1.13.0)\n",
      "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->timm) (3.3)\n",
      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->timm) (3.1.4)\n",
      "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch->timm)\n",
      "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
      "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch->timm)\n",
      "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
      "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch->timm)\n",
      "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
      "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch->timm)\n",
      "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
      "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch->timm)\n",
      "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
      "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch->timm)\n",
      "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
      "Collecting nvidia-curand-cu12==10.3.2.106 (from torch->timm)\n",
      "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
      "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch->timm)\n",
      "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
      "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch->timm)\n",
      "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
      "Collecting nvidia-nccl-cu12==2.20.5 (from torch->timm)\n",
      "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
      "Collecting nvidia-nvtx-cu12==12.1.105 (from torch->timm)\n",
      "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
      "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch->timm) (2.3.0)\n",
      "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch->timm)\n",
      "  Downloading nvidia_nvjitlink_cu12-12.5.82-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m63.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision->timm) (1.25.2)\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision->timm) (9.4.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->timm) (2.1.5)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->timm) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->timm) (3.7)\n",
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      "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, timm\n",
      "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 timm-1.0.7\n",
      "Collecting vtk\n",
      "  Downloading vtk-9.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (92.1 MB)\n",
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      "Installing collected packages: vtk\n",
      "Successfully installed vtk-9.3.1\n",
      "Collecting torch_geometric\n",
      "  Downloading torch_geometric-2.5.3-py3-none-any.whl (1.1 MB)\n",
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      "Installing collected packages: torch_geometric\n",
      "Successfully installed torch_geometric-2.5.3\n",
      "Collecting meshio\n",
      "  Downloading meshio-5.3.5-py3-none-any.whl (166 kB)\n",
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      "Installing collected packages: meshio\n",
      "Successfully installed meshio-5.3.5\n",
      "Collecting einops\n",
      "  Downloading einops-0.8.0-py3-none-any.whl (43 kB)\n",
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      "\u001b[?25hInstalling collected packages: einops\n",
      "Successfully installed einops-0.8.0\n"
     ]
    }
   ],
   "source": [
    "!pip install timm\n",
    "!pip install vtk\n",
    "!pip install torch_geometric\n",
    "!pip install meshio\n",
    "!pip install einops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pRCQC1ILfBR7"
   },
   "source": [
    "# **模型训练**\n",
    "\n",
    "训练前请确保工作目录在GeoCAD_V2下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6TZ6_vLitf6N"
   },
   "outputs": [],
   "source": [
    "import train\n",
    "import os\n",
    "import torch\n",
    "import argparse\n",
    "import yaml\n",
    "from geom.pc_encoder import load_geom_encoder\n",
    "from dataset import read_data, GraphDataset\n",
    "from model import GeoCA3D\n",
    "from cfd.models.utils import MLP\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 417,
     "status": "ok",
     "timestamp": 1720840508142,
     "user": {
      "displayName": "Yuanwei Bin",
      "userId": "04820485600131748919"
     },
     "user_tz": -480
    },
    "id": "uUY5cZhouxTz",
    "outputId": "a077ec1d-3a31-4940-f2b5-9734d9c28c0a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Namespace(train_data_dir='../Dataset/Trainset_track_B', test_data_dir='../Dataset/Testset_track_B/Inference', info_dir='../Dataset/Testset_track_B/Auxiliary', extra_data_dir=None, fold_id=0, gpu=0, val_iter=10, config_dir='params.yaml', ulip_model='ULIP_PointBERT', ulip_ckpt='geom/ckpt/pretrained_models_ckpt_zero-sho_classification_checkpoint_pointbert.pt', frozen=True, cfd_config_dir='cfd/cfd_params.yaml', cfd_model='MLP', cfd_mesh=True, weight=0.5, loadPretrainedMLP=False, PretrainedMLPPath=None)\n"
     ]
    }
   ],
   "source": [
    "sys.argv = [\n",
    "    'script.py',\n",
    "    '--train_data_dir', '../Dataset/Trainset_track_B',\n",
    "    '--test_data_dir', '../Dataset/Testset_track_B/Inference',\n",
    "]\n",
    "parser = argparse.ArgumentParser()\n",
    "parser.add_argument('--train_data_dir', default='../Dataset/Trainset_track_B')\n",
    "parser.add_argument('--test_data_dir', default='../Dataset/Testset_track_B/Inference')\n",
    "parser.add_argument('--info_dir', default='../Dataset/Testset_track_B/Auxiliary')\n",
    "parser.add_argument('--extra_data_dir', default=None)\n",
    "parser.add_argument('--fold_id', default=0, type=int)\n",
    "parser.add_argument('--gpu', default=0, type=int)\n",
    "parser.add_argument('--val_iter', default=10, type=int)\n",
    "parser.add_argument('--config_dir', default='params.yaml')\n",
    "parser.add_argument('--ulip_model', default='ULIP_PointBERT')\n",
    "parser.add_argument('--ulip_ckpt', default='geom/ckpt/pretrained_models_ckpt_zero-sho_classification_checkpoint_pointbert.pt')\n",
    "parser.add_argument('--frozen', default=True)\n",
    "parser.add_argument('--cfd_config_dir', default='cfd/cfd_params.yaml')\n",
    "parser.add_argument('--cfd_model', default='MLP')\n",
    "parser.add_argument('--cfd_mesh', default=True)\n",
    "parser.add_argument('--weight', default=0.5, type=float)\n",
    "parser.add_argument('--loadPretrainedMLP', default=False)\n",
    "parser.add_argument('--PretrainedMLPPath', default=None)\n",
    "\n",
    "args = parser.parse_args()\n",
    "print(args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 858,
     "status": "ok",
     "timestamp": 1720840513506,
     "user": {
      "displayName": "Yuanwei Bin",
      "userId": "04820485600131748919"
     },
     "user_tz": -480
    },
    "id": "4pVmLWxkv9Ul",
    "outputId": "ffc518f9-19ab-4732-e958-e40186bc4c94"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'encoder': [3, 64, 64, 32, 64], 'decoder': [64, 32, 64, 64, 1], 'nb_hidden_layers': 12, 'size_hidden_layers': 64, 'batch_size': 1, 'nb_epochs': 1000, 'lr': 0.001, 'bn_bool': True, 'res_bool': False, 'r': 0.02}\n"
     ]
    }
   ],
   "source": [
    "with open(args.cfd_config_dir, 'r') as f:\n",
    "    cfd_hparams = yaml.safe_load(f)[args.cfd_model]\n",
    "print(cfd_hparams)\n",
    "\n",
    "with open(args.config_dir, 'r') as f:\n",
    "    hparams = yaml.safe_load(f)['GeoCA3D']\n",
    "\n",
    "n_gpu = torch.cuda.device_count()\n",
    "use_cuda = 0 <= args.gpu < n_gpu and torch.cuda.is_available()\n",
    "device = torch.device(f'cuda:{args.gpu}' if use_cuda else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "y_G_hMtpwUMp"
   },
   "outputs": [],
   "source": [
    "train_data, val_data, test_data, coef_norm, test_index = read_data(args, norm=True)\n",
    "\n",
    "use_height = False\n",
    "r = cfd_hparams['r'] if 'r' in cfd_hparams.keys() else None\n",
    "train_ds = GraphDataset(train_data, use_height=use_height, use_cfd_mesh=args.cfd_mesh, r=r)\n",
    "val_ds = GraphDataset(val_data, use_height=use_height, use_cfd_mesh=args.cfd_mesh, r=r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3797,
     "status": "ok",
     "timestamp": 1720770122778,
     "user": {
      "displayName": "Yuanwei Bin",
      "userId": "04820485600131748919"
     },
     "user_tz": -480
    },
    "id": "tSOk5TKQwWHG",
    "outputId": "4f97b7c3-b12c-47b3-8046-efab55565dd1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=> creating model: ULIP_PointBERT\n",
      "=> loaded resume checkpoint 'geom/ckpt/pretrained_models_ckpt_zero-sho_classification_checkpoint_pointbert.pt'\n",
      "{'in_out_dim': 32, 'dropout': 0.1, 'context_dim': 512, 'gated_ff': True}\n",
      "cfd_hparams {'encoder': [3, 64, 64, 32, 32], 'decoder': [32, 32, 64, 64, 1], 'nb_hidden_layers': 12, 'size_hidden_layers': 32, 'batch_size': 1, 'nb_epochs': 1000, 'lr': 0.01, 'bn_bool': True, 'res_bool': False, 'r': 0.02}\n"
     ]
    }
   ],
   "source": [
    "if args.ulip_model == 'none':\n",
    "    g_encoder, g_proj = None, None\n",
    "else:\n",
    "    g_encoder, g_proj = load_geom_encoder(args, pretrained=True, frozen=args.frozen)\n",
    "    print(hparams)\n",
    "\n",
    "encoder = MLP(cfd_hparams['encoder'], batch_norm=False)\n",
    "decoder = MLP(cfd_hparams['decoder'], batch_norm=False)\n",
    "\n",
    "if args.cfd_model == 'GraphSAGE':\n",
    "    from cfd.models.GraphSAGE import GraphSAGE\n",
    "    model = GraphSAGE(cfd_hparams, encoder, decoder)\n",
    "elif args.cfd_model == 'MLP':\n",
    "    from cfd.models.NN import NN\n",
    "    model = NN(cfd_hparams, encoder, decoder)\n",
    "    print('cfd_hparams',cfd_hparams)\n",
    "elif args.cfd_model == 'GAT':\n",
    "    from cfd.models.GAT import GAT\n",
    "    model = GAT(cfd_hparams, encoder, decoder)\n",
    "elif args.cfd_model == 'GNO':\n",
    "    from cfd.models.GNO import GNO\n",
    "    model = GNO(cfd_hparams, encoder, decoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cbNVg6sxxrOp"
   },
   "outputs": [],
   "source": [
    "model = GeoCA3D(model, geom_encoder=g_encoder, geom_proj=g_proj, **hparams)\n",
    "path = f'metrics/TrackB/{args.cfd_model}_{args.ulip_model}_frozens_{args.frozen}'\n",
    "if not os.path.exists(path):\n",
    "    os.makedirs(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 245
    },
    "executionInfo": {
     "elapsed": 16415289,
     "status": "error",
     "timestamp": 1720786604302,
     "user": {
      "displayName": "Yuanwei Bin",
      "userId": "04820485600131748919"
     },
     "user_tz": -480
    },
    "id": "AMeJpdjzbYWR",
    "outputId": "6041a76c-668b-4693-d37a-c3453c01b0a6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8%|▊         | 85/1000 [4:33:34<49:05:00, 193.12s/it, train_loss=0.203]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-72-d972890cf89d>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcfd_hparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mval_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoef_norm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoef_norm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrozen\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrozen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpretrainedMLP\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadPretrainedMLP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpretrainedMLPPath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPretrainedMLPPath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/content/drive/MyDrive/Colab Notebooks/train.py\u001b[0m in \u001b[0;36mmain\u001b[0;34m(device, train_dataset, val_dataset, Net, hparams, path, reg, val_iter, coef_norm, frozen, pretrainedMLP, pretrainedMLPPath)\u001b[0m\n",
      "\u001b[0;32m/content/drive/MyDrive/Colab Notebooks/train.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(device, model, train_loader, coef_norm, optimizer, scheduler, reg, frozen)\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model = train.main(device, train_ds, val_ds, model, cfd_hparams, path, val_iter=args.val_iter, reg=args.weight, coef_norm=coef_norm, frozen=args.frozen, pretrainedMLP=args.loadPretrainedMLP, pretrainedMLPPath=args.PretrainedMLPPath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wUYMX0ohurRr"
   },
   "source": [
    "# **模型微调**\n",
    "使用额外数据对模型进行微调"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TM0IjfRRumqH"
   },
   "outputs": [],
   "source": [
    "args.loadPretrainedMLP = True\n",
    "args.PretrainedMLPPath = f'metrics/model_0.1486660446842386.pth'\n",
    "args.extra_data_dir = '../Dataset/Extra_Trainset_track_B'\n",
    "\n",
    "###载入额外数据\n",
    "train_data, val_data, test_data, coef_norm, test_index = read_data(args, norm=True)\n",
    "use_height = False\n",
    "r = cfd_hparams['r'] if 'r' in cfd_hparams.keys() else None\n",
    "train_ds = GraphDataset(train_data, use_height=use_height, use_cfd_mesh=args.cfd_mesh, r=r)\n",
    "val_ds = GraphDataset(val_data, use_height=use_height, use_cfd_mesh=args.cfd_mesh, r=r)\n",
    "\n",
    "\n",
    "###载入已训练模型进行微调\n",
    "model = train.main(device, train_ds, val_ds, model, cfd_hparams, path, val_iter=args.val_iter, reg=args.weight, coef_norm=coef_norm, frozen=args.frozen, pretrainedMLP=args.loadPretrainedMLP, pretrainedMLPPath=args.PretrainedMLPPath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Igh5PjGBeUVD"
   },
   "source": [
    "# **推理部分**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "executionInfo": {
     "elapsed": 11721,
     "status": "ok",
     "timestamp": 1720951147906,
     "user": {
      "displayName": "Yuanwei Bin",
      "userId": "04820485600131748919"
     },
     "user_tz": -480
    },
    "id": "4DmO0j-kgTBM"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import argparse\n",
    "import yaml\n",
    "from geom.pc_encoder import load_geom_encoder\n",
    "from dataset import read_data, GraphDataset\n",
    "from model import GeoCA3D\n",
    "from cfd.models.utils import MLP\n",
    "import time\n",
    "import numpy as np\n",
    "import sys\n",
    "from torch_geometric.loader import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 732,
     "status": "ok",
     "timestamp": 1720951158849,
     "user": {
      "displayName": "Yuanwei Bin",
      "userId": "04820485600131748919"
     },
     "user_tz": -480
    },
    "id": "11xFFaEsfsZ_",
    "outputId": "3a5f4e7e-4c79-4977-eac7-69bafc99185b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Namespace(train_data_dir='../Dataset/Trainset_track_B', test_data_dir='../Dataset/Testset_track_B/Inference', info_dir='../Dataset/Testset_track_B/Auxiliary', extra_data_dir=None, fold_id=0, gpu=0, val_iter=10, config_dir='params.yaml', ulip_model='ULIP_PointBERT', ulip_ckpt='geom/ckpt/pretrained_models_ckpt_zero-sho_classification_checkpoint_pointbert.pt', frozen=True, cfd_config_dir='cfd/cfd_params.yaml', cfd_model='MLP', cfd_mesh=True, weight=0.5)\n",
      "{'encoder': [3, 64, 64, 32, 64], 'decoder': [64, 32, 64, 64, 1], 'nb_hidden_layers': 12, 'size_hidden_layers': 64, 'batch_size': 1, 'nb_epochs': 1000, 'lr': 0.001, 'bn_bool': True, 'res_bool': False, 'r': 0.02}\n"
     ]
    }
   ],
   "source": [
    "sys.argv = [\n",
    "    'script.py',\n",
    "    '--train_data_dir', '../Dataset/Trainset_track_B',\n",
    "    '--test_data_dir', '../Dataset/Testset_track_B/Inference',\n",
    "]\n",
    "parser = argparse.ArgumentParser()\n",
    "parser.add_argument('--train_data_dir', default='../Dataset/Trainset_track_B')\n",
    "parser.add_argument('--test_data_dir', default='../Dataset/Testset_track_B/Inference')\n",
    "parser.add_argument('--info_dir', default='../Dataset/Testset_track_B/Auxiliary')\n",
    "parser.add_argument('--extra_data_dir', default=None)\n",
    "parser.add_argument('--fold_id', default=0, type=int)\n",
    "parser.add_argument('--gpu', default=0, type=int)\n",
    "parser.add_argument('--val_iter', default=10, type=int)\n",
    "parser.add_argument('--config_dir', default='params.yaml')\n",
    "parser.add_argument('--ulip_model', default='ULIP_PointBERT')\n",
    "parser.add_argument('--ulip_ckpt', default='geom/ckpt/pretrained_models_ckpt_zero-sho_classification_checkpoint_pointbert.pt')\n",
    "parser.add_argument('--frozen', default=True)\n",
    "parser.add_argument('--cfd_config_dir', default='cfd/cfd_params.yaml')\n",
    "parser.add_argument('--cfd_model', default='MLP')\n",
    "parser.add_argument('--cfd_mesh', default=True)\n",
    "parser.add_argument('--weight', default=0.5, type=float)\n",
    "\n",
    "args = parser.parse_args()\n",
    "print(args)\n",
    "\n",
    "with open(args.cfd_config_dir, 'r') as f:\n",
    "    cfd_hparams = yaml.safe_load(f)[args.cfd_model]\n",
    "print(cfd_hparams)\n",
    "\n",
    "with open(args.config_dir, 'r') as f:\n",
    "    hparams = yaml.safe_load(f)['GeoCA3D']\n",
    "\n",
    "n_gpu = torch.cuda.device_count()\n",
    "use_cuda = 0 <= args.gpu < n_gpu and torch.cuda.is_available()\n",
    "device = torch.device(f'cuda:{args.gpu}' if use_cuda else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/",
     "height": 529
    },
    "executionInfo": {
     "elapsed": 138999,
     "status": "error",
     "timestamp": 1720951301027,
     "user": {
      "displayName": "Yuanwei Bin",
      "userId": "04820485600131748919"
     },
     "user_tz": -480
    },
    "id": "rRCdDKrUf44x"
   },
   "outputs": [],
   "source": [
    "#####载入数据\n",
    "train_data, val_data, test_data, coef_norm, test_index = read_data(args, norm=True)\n",
    "\n",
    "use_height = False\n",
    "r = cfd_hparams['r'] if 'r' in cfd_hparams.keys() else None\n",
    "train_ds = GraphDataset(train_data, use_height=use_height, use_cfd_mesh=args.cfd_mesh, r=r)\n",
    "test_ds = GraphDataset(test_data, use_height=use_height, use_cfd_mesh=args.cfd_mesh, r=r)\n",
    "val_ds = GraphDataset(val_data, use_height=use_height, use_cfd_mesh=args.cfd_mesh, r=r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 321
    },
    "executionInfo": {
     "elapsed": 2664,
     "status": "error",
     "timestamp": 1720950926077,
     "user": {
      "displayName": "Yuanwei Bin",
      "userId": "04820485600131748919"
     },
     "user_tz": -480
    },
    "id": "TqRlH-dufJR7",
    "outputId": "45f0c2ef-2e6d-466b-fa04-607c1f8cc5b3"
   },
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-e20c51ac7c55>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf'metrics/model_0.13985563115390126.pth'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, weights_only, mmap, **pickle_load_args)\u001b[0m\n\u001b[1;32m   1023\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mRuntimeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUnpicklingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mUNSAFE_MESSAGE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1025\u001b[0;31m                 return _load(opened_zipfile,\n\u001b[0m\u001b[1;32m   1026\u001b[0m                              \u001b[0mmap_location\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1027\u001b[0m                              \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_load\u001b[0;34m(zip_file, map_location, pickle_module, pickle_file, overall_storage, **pickle_load_args)\u001b[0m\n\u001b[1;32m   1444\u001b[0m     \u001b[0munpickler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUnpicklerWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_load_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1445\u001b[0m     \u001b[0munpickler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpersistent_load\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpersistent_load\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1446\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munpickler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1448\u001b[0m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_loaded_sparse_tensors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36mpersistent_load\u001b[0;34m(saved_id)\u001b[0m\n\u001b[1;32m   1414\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1415\u001b[0m             \u001b[0mnbytes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumel\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_element_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1416\u001b[0;31m             \u001b[0mtyped_storage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_maybe_decode_ascii\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1417\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1418\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mtyped_storage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36mload_tensor\u001b[0;34m(dtype, numel, key, location)\u001b[0m\n\u001b[1;32m   1388\u001b[0m         \u001b[0;31m# stop wrapping with TypedStorage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1389\u001b[0m         typed_storage = torch.storage.TypedStorage(\n\u001b[0;32m-> 1390\u001b[0;31m             \u001b[0mwrap_storage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrestore_location\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1391\u001b[0m             \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1392\u001b[0m             _internal=True)\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36mdefault_restore_location\u001b[0;34m(storage, location)\u001b[0m\n\u001b[1;32m    388\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdefault_restore_location\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_package_registry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 390\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    391\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_cuda_deserialize\u001b[0;34m(obj, location)\u001b[0m\n\u001b[1;32m    263\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_cuda_deserialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cuda'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 265\u001b[0;31m         \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidate_cuda_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    266\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_torch_load_uninitialized\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36mvalidate_cuda_device\u001b[0;34m(location)\u001b[0m\n\u001b[1;32m    247\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    248\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 249\u001b[0;31m         raise RuntimeError('Attempting to deserialize object on a CUDA '\n\u001b[0m\u001b[1;32m    250\u001b[0m                            \u001b[0;34m'device but torch.cuda.is_available() is False. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m                            \u001b[0;34m'If you are running on a CPU-only machine, '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU."
     ]
    }
   ],
   "source": [
    "path = f'metrics/model_0.13985563115390126.pth'\n",
    "model = torch.load(path).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ulRzSVAT7RD7"
   },
   "outputs": [],
   "source": [
    "#####模型推理\n",
    "model.eval()\n",
    "index = 0\n",
    "\n",
    "if not os.path.exists('../B_Results'):\n",
    "    os.makedirs('../B_Results')\n",
    "\n",
    "test_loader = DataLoader(test_ds, batch_size=1)\n",
    "with torch.no_grad():\n",
    "  for cfd_data, geom in test_loader:\n",
    "    cfd_data = cfd_data.to(device)\n",
    "    geom = geom.to(device)\n",
    "    out = model((cfd_data, geom))\n",
    "    if coef_norm is not None:\n",
    "      mean = torch.tensor(coef_norm[2]).to(device)\n",
    "      std = torch.tensor(coef_norm[3]).to(device)\n",
    "      pred_press = out * std[-1] + mean[-1]\n",
    "      np.save(f\"../B_Results/press_{test_index[index]}.npy\", pred_press.detach().cpu().numpy().squeeze())\n",
    "      index = index + 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pbGNuQmG86E7"
   },
   "outputs": [],
   "source": [
    "!zip -r ../B_Results.zip ../B_Results"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "machine_shape": "hm",
   "name": "",
   "version": ""
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
